Search for Fourth Generation Quarks at CMS

We summarise the analyses that search for fourth generation quarks at the Central Muon Solenoid (CMS) experiment. Such particles provide a natural extension to the Standard Model (SM) and are still consistent with precision electroweak measurements. Our searches are not limited to fourth generation chiral quarks and are relevant to many Beyond the Standard Model theories. No excess over the expected SM background is observed in any of these analyses and limits are set on the masses of the $b^\prime$ and $t^\prime$ quarks at 95% confidence level at 361 GeV/$c^2$ and 450 GeV/$c^2$, respectively.Comment: 10 pages, 6 figures, Proceedings of the DPF-2011 Conference, Providence, RI, August 8-13, 201

Optically controlled orbital angular momentum generation in a polaritonic quantum fluid

Applications of the orbital angular momentum (OAM) of light range from the next generation of optical communication systems to optical imaging and optical manipulation of particles. Here we propose a micron-sized semiconductor source which emits light with pre-defined OAM components. This source is based on a polaritonic quantum fluid. We show how in this system modulational instabilities can be controlled and harnessed for the spontaneous formation of OAM components not present in the pump laser source. Once created, the OAM states exhibit exotic flow patterns in the quantum fluid, characterized by generation-annihilation pairs. These can only occur in open systems, not in equilibrium condensates, in contrast to well-established vortex-antivortex pairs

Directional optical switching and transistor functionality using optical parametric oscillation in a spinor polariton fluid

Over the past decade, spontaneously emerging patterns in the density of polaritons in semiconductor microcavities were found to be a promising candidate for all-optical switching. But recent approaches were mostly restricted to scalar fields, did not benefit from the polariton's unique spin-dependent properties, and utilized switching based on hexagon far-field patterns with 60{\deg} beam switching (i.e. in the far field the beam propagation direction is switched by 60{\deg}). Since hexagon far-field patterns are challenging, we present here an approach for a linearly polarized spinor field, that allows for a transistor-like (e.g., crucial for cascadability) orthogonal beam switching, i.e. in the far field the beam is switched by 90{\deg}. We show that switching specifications such as amplification and speed can be adjusted using only optical means

High-performance FPGA-based accelerator for Bayesian neural networks

Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs are unable to capture their model uncertainty which is crucial for many safety-critical applications including healthcare and autonomous vehicles. In comparison, Bayesian neural networks (BNNs) are able to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their expensive computational cost and limited hardware performance. This work proposes a novel FPGA based hardware architecture to accelerate BNNs inferred through Monte Carlo Dropout. Compared with other state-of-the-art BNN accelerators, the proposed accelerator can achieve up to 4 times higher energy efficiency and 9 times better compute efficiency. Considering partial Bayesian inference, an automatic framework is proposed, which explores the trade-off between hardware and algorithmic performance. Extensive experiments are conducted to demonstrate that our proposed framework can effectively find the optimal points in the design space

High-Performance FPGA-based Accelerator for Bayesian Neural Networks

Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs are unable to capture their model uncertainty which is crucial for many safety-critical applications including healthcare and autonomous vehicles. In comparison, Bayesian neural networks (BNNs) are able to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their expensive computational cost and limited hardware performance. This work proposes a novel FPGA based hardware architecture to accelerate BNNs inferred through Monte Carlo Dropout. Compared with other state-of-the-art BNN accelerators, the proposed accelerator can achieve up to 4 times higher energy efficiency and 9 times better compute efficiency. Considering partial Bayesian inference, an automatic framework is proposed, which explores the trade-off between hardware and algorithmic performance. Extensive experiments are conducted to demonstrate that our proposed framework can effectively find the optimal points in the design space

Sampling Distributions of Random Electromagnetic Fields in Mesoscopic or Dynamical Systems

We derive the sampling probability density function (pdf) of an ideal localized random electromagnetic field, its amplitude and intensity in an electromagnetic environment that is quasi-statically time-varying statistically homogeneous or static statistically inhomogeneous. The results allow for the estimation of field statistics and confidence intervals when a single spatial or temporal stochastic process produces randomization of the field. Results for both coherent and incoherent detection techniques are derived, for Cartesian, planar and full-vectorial fields. We show that the functional form of the sampling pdf depends on whether the random variable is dimensioned (e.g., the sampled electric field proper) or is expressed in dimensionless standardized or normalized form (e.g., the sampled electric field divided by its sampled standard deviation). For dimensioned quantities, the electric field, its amplitude and intensity exhibit different types of Bessel $K$ sampling pdfs, which differ significantly from the asymptotic Gauss normal and $\chi^{(2)}_{2p}$ ensemble pdfs when $\nu$ is relatively small. By contrast, for the corresponding standardized quantities, Student $t$, Fisher-Snedecor $F$ and root-$F$ sampling pdfs are obtained that exhibit heavier tails than comparable Bessel $K$ pdfs. Statistical uncertainties obtained from classical small-sample theory for dimensionless quantities are shown to be overestimated compared to dimensioned quantities. Differences in the sampling pdfs arising from de-normalization versus de-standardization are obtained.Comment: 12 pages, 15 figures, accepted for publication in Phys. Rev. E, minor typos correcte

Kaluza-Klein towers for real vector fields in flat space

We consider a free real vector field propagating in a five dimensional flat space with its fifth dimension compactified either on a strip or on a circle and perform a Kalaza Klein reduction which breaks SO(4,1) invariance while reserving SO(3,1) invariance. Taking into account the Lorenz gauge condition, we obtain from the most general hermiticity conditions for the relevant operators all the allowed boundary conditions which have to be imposed on the fields in the extra-dimension. The physical Kaluza-Klein mass towers, which result in a four-dimensional brane, are determined in the different distinct allowed cases. They depend on the bulk mass, on the parameters of the boundary conditions and on the extra parameter present in the Lagrangian. In general, they involve vector states together with accompanying scalar states.Comment: 28 pages, 4 independent table

Preventing Fusion Mass Shift Avoids Postoperative Distal Curve Adding-on in Adolescent Idiopathic Scoliosis

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